Artificial neural networks (ANNs) have emerged as an essential tool in machine learning,
achieving remarkable success across diverse domains, including image and speech …

In theoretical neuroscience, recent work leverages deep learning tools to explore how some
network attributes critically influence its learning dynamics. Notably, initial weight …

The spectacular successes of recurrent neural network models where key parameters are
adjusted via backpropagation-based gradient descent have inspired much thought as to …

Recent studies show that, even in constant environments, the tuning of single neurons
changes over time in a variety of brain regions. This representational drift has been …

Training networks consisting of biophysically accurate neuron models could allow for new
insights into how brain circuits can organize and solve tasks. We begin by analyzing the …

Recurrent neural networks exhibit chaotic dynamics when the variance in their connection
strengths exceed a critical value. Recent work indicates connection variance also modulates …

The impact of initial connectivity on learning has been extensively studied in the context of
backpropagation-based gradient descent, but it remains largely underexplored in …

The brain's prowess in learning and adapting remains an enigma, particularly in its
approach to the'temporal credit assignment'problem. How do neural circuits determine …

How do brain circuits learn to generate behaviour? While significant strides have been
made in understanding learning in artificial neural networks, applying this knowledge to …